Update app_langgraph.py

app_langgraph.py

@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.prebuilt import tools_condition
 from langgraph.prebuilt import ToolNode
-# from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace, HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace, HuggingFaceEmbeddings
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.globals import set_debug
 from langchain_groq import ChatGroq
@@ -15,6 +15,7 @@ from tools.math_tools import multiply, add, subtract, divide
 # from langchain_community.vectorstores import SupabaseVectorStore
 import json
 from tools.multimodal_tools import extract_text, analyze_image_tool, analyze_audio_tool
+from langchain_google_genai import ChatGoogleGenerativeAI
 
 # set_debug(True)
 load_dotenv()
@@ -34,6 +35,7 @@ tools = [
 
 def build_graph():
     hf_token = os.getenv("HF_TOKEN")
+    api_key = os.getenv("GEMINI_API_KEY")
     # llm = HuggingFaceEndpoint(
     #     repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
     #     huggingfacehub_api_token=hf_token,
@@ -42,15 +44,24 @@ def build_graph():
     # chat = ChatHuggingFace(llm=llm, verbose=True)
     # llm_with_tools = chat.bind_tools(tools)
 
-    llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
-    llm_with_tools = llm.bind_tools(tools)
+    # llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
+    # llm_with_tools = llm.bind_tools(tools)
+
+    chat = ChatGoogleGenerativeAI(
+        model= "gemini-2.5-pro-preview-05-06",
+        temperature=0,
+        max_retries=2,
+        google_api_key=api_key,
+        thinking_budget= 0
+    )
+    chat_with_tools = chat.bind_tools(tools)
 
     def assistant(state: MessagesState):
         sys_msg = "You are a helpful assistant with access to tools. Understand user requests accurately. Use your tools when needed to answer effectively. Strictly follow all user instructions and constraints." \
         "Pay attention: your output needs to contain only the final answer without any reasoning since it will be strictly evaluated against a dataset which contains only the specific response." \
         "Your final output needs to be just the string or integer containing the answer, not an array or technical stuff."
         return {
-            "messages": [llm_with_tools.invoke([sys_msg] + state["messages"])],
+            "messages": [chat_with_tools.invoke([sys_msg] + state["messages"])],
         }
 
     ## The graph